Field of the Invention
The present invention relates to an information processing field, and more particularly to a method and apparatus for generating recommended items in an unfamiliar domain.
Description of Related Art
Traditional methods of generating recommended items are based on statistics of behaviors of many users, and items are recommended in a form of ranking list, best-selling list or the like. Some of those methods are described below.
U.S. Published Application No. 2005/0256756 A1 predicts a user's rating of a new item in a collaborative filtering system. The invention incorporates social network information in addition to user ratings to make recommendations. The distance between users in the social network is used to enhance the estimate of user similarities for collaborative filtering. The social network can be constructed explicitly by users or deduced implicitly from observed interaction between users.
U.S. Published Application No. 2003/0149612 A1 describes a method for rating an item within a recommendation system. In a recommendation scheme, each of a multitude of users U and each of a multitude of items I is included in a profile P(U,I) that includes ratings. Based on the similarity between a given user and the multitude of users in terms of the ratings, a subset of users is selected who have interest similar to those of the given user.
U.S. Pat. No. 6,321,049 describes a method for recommending items to users using automated collaborative filtering stores profiles of users relating ratings to items in memory. Profiles of items may also be stored in memory, the item profiles associating users with the rating given to the item by that user or inferred for the user by the system. The user profiles include additional information relating to the user or associated with the rating given to an item by the user. Item profiles are retrieved to determine which users have rated a particular item. Profiles of those users are accessed and the ratings are used to calculate similarity factors with respect to other users. The similarity factors, sometimes in connection with confidence factors, are used to select a set of neighboring users. The neighboring users are weighted based on their respective similarity factors, and a rating for an item contained in the domain is predicted. In one embodiment, items in the domain have features. In this embodiment, the values for features can be clustered, and the similarity factors incorporate assigned feature weights and feature value cluster weights. In some embodiments, item concepts are used to enhance recommendation accuracy.
U.S. Pat. No. 6,321,179 B1 describes a method of providing predicted user ratings includes calculating the accuracy of predictions based on the variance of distribution of the predicted user's rating. The system and method present and rank the results by treating the variance as a source of noise. The decision to present or not to present an item is made by sampling the probability distribution of the predicted rating and comparing the result to some user-set threshold (e.g., “show me all results that the system predicts I will score 3 or higher”) or a system default value.